1. Field of the Invention
The present invention generally relates to cognitive radio networks. More specifically, the present invention relates to implementing a multi-user modulation classification in a distributed cognitive radio network.
2. Description of the Related Art
Signal classification is an important component in cognitive radio networks. Cognitive radios are intelligent radio transceivers capable of sensing wireless signals in an operating frequency band and adapting their transmission parameters responsive to the sensed wireless signals. In order to make such responsive decisions, intelligent radio devices like cognitive radios need to understand which devices currently occupy particular radio frequency bands. Cognitive radios may further wish to find compatible devices with which to communicate, locate unoccupied frequencies, or avoid bands occupied by certain types of transmitting or receiving devices due to interference.
Automatic Modulation Classification (AMC) techniques have traditionally been used in cognitive radio networks to recognize the modulation scheme of unknown transmitted signals. Recognition of modulation schemes for an unknown signal is generally considered an intermediate step between signal detection and demodulation. AMC is used in a variety of cognitive radio networks, including civilian, commercial, and even military.
AMC algorithms (i.e., classification techniques) can be broadly classified in two categories: likelihood-based approaches and feature-based approaches. Likelihood-based approaches characterize the likelihood function of a received waveform conditioned on a particular constellation format. Feature-based approaches, on the other hand, rely on a set of features to perform a classification task.
Feature-based AMCs are more widely used because of their ease of implementation and manageable computational complexity. Higher order statistics, cyclostationary features, and wavelet features are a few of the more commonly used feature-based AMCs. But while a body of research exists with respect to the use of AMC algorithms in cognitive radio networks, the detection and classification of signals from more than one user within a certain frequency band has yet to receive widespread adoption much less particular attention.
For example, simulations have been performed using a distributed algorithm for cooperative modulation classification using an iterative Method of Multipliers (MoM) optimization algorithm. In such a simulation, individual nodes exchange data and each independently reach (presumably) the same decision. This iterative approach is ineffective, however, in that it imposes a high overhead on the wireless network.
Alternative simulations that utilized a more centralized approach have been studied. For example, simulations have occurred where a centralized data node collects decisions rendered by individual nodes. While this approach minimizes network overhead by not requiring each individual node to exchange data with one another, the centralized node merely combines the final decisions of the distributed cognitive radio nodes thereby ignoring a wealth of information contained in soft data.
As the radio frequency spectrum becomes more congested with traffic and interference, it becomes increasingly likely that multiple signals will simultaneously co-exist within the same band. And as cognitive radios become more commonplace, there will be a corresponding demand for modulation classification to allow for navigation around highly congested or otherwise interference laden networks. Increasing demands for spectrum management and policy application will likewise require additional network detail and information not available from presently utilized algorithms. There is thus a need in the art for detection and classification of multi-user signals within a frequency band.